QSPR nobel study of water solubility of diverse functional acyclic compounds

This work required to assign the training set of compounds to one or more activity classes' Specific descriptor centers also can be defined if desired. The automated pharmacophore identification portion of the program then builds the knowledge base using the following steps: Identify all possible binding interaction centers for each compound in the data set; Generate topological (2D) or topographical (3D) distance matrices based on the set of descriptors; Identify possible pharmacophores from all pairs of molecules using clique selection algorithms;Classify these pharmacophores based upon their occurance in compounds in each activity class using Bayesian statistics and their nonchanceoccurance; Set thresholds for probability and reliability statistics associated with a pharmacophore so that all training set molecules are properly classified by the pharmacophore rules; Align compounds containing high probability pharmacophores on the pharmacophore.Basic macroscopic properties: Molar Volume (MV), Molar Refractivity (MR) and Parachor (Pr);Derived macroscopic properties: density (d), refractive index (n) and surface tension (M1T–2). Once the knowledge base has been constructed, the scientist can use it to predict biological activity for compounds not included in the training set.